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Table 1 Determinants that a given Stegodyphus occurrence record belongs to a social species, assessed by logistic regression modelling with information-theoretic model selection

From: Habitat productivity constrains the distribution of social spiders across continents – case study of the genus Stegodyphus

Model/predictors K ∆ AIC w i TSS R2
Region    GVI   SF 9 0.321 0.271 0.303 0.425
Region    GVI I (Region* GVI) SF 10 2.069 0.113 0.318 0.425
Region PSea   GVI   SF 10 1.965 0.119 0.308 0.425
Region PSea I (Region* PSea) GVI I (Region* GVI) SF 12 5.260 0.023 0.324 0.427
Region PSea     SF 9 20.886 0.000 0.328 0.371
Region PSea I (Region* PSea)    SF 10 22.727 0.000 0.306 0.372
  PSea   GVI   SF 8 1.406 0.157 0.305 0.417
    GVI   SF 7 0.000 0.318 0.306 0.415
  PSea     SF 7 18.428 0.000 0.342 0.368
      SF 6 19.752 0.000 0.355 0.359
  1. Abbreviations of the predictors used in each of the 10 models for logit link, are as follows (without the intercept): Region for regional variable (combination of 2 binaries); GVI for vegetation productivity; PSea for precipitation seasonality; I (Region* GVI and Region*PSea) for interaction terms; and SF for six spatial filters used. Best supported models are shown in bold. K is the number of model parameters, ∆ AIC are AIC differences, wi Akaike weights of each model; and TSS is the true skill statistics score of each model (see Methods section for more explanation on the last three).